Overview

Dataset statistics

Number of variables16
Number of observations84464
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 MiB
Average record size in memory128.0 B

Variable types

Categorical5
Numeric11

Alerts

Month has a high cardinality: 453 distinct valuesHigh cardinality
ForeignPort has a high cardinality: 192 distinct valuesHigh cardinality
Country has a high cardinality: 74 distinct valuesHigh cardinality
Passengers_In is highly overall correlated with Freight_In_(tonnes) and 6 other fieldsHigh correlation
Freight_In_(tonnes) is highly overall correlated with Passengers_In and 7 other fieldsHigh correlation
Mail_In_(tonnes) is highly overall correlated with Passengers_In and 7 other fieldsHigh correlation
Passengers_Out is highly overall correlated with Passengers_In and 6 other fieldsHigh correlation
Freight_Out_(tonnes) is highly overall correlated with Passengers_In and 7 other fieldsHigh correlation
Mail_Out_(tonnes) is highly overall correlated with Freight_In_(tonnes) and 4 other fieldsHigh correlation
Passengers_Total is highly overall correlated with Passengers_In and 6 other fieldsHigh correlation
Freight_Total_(tonnes) is highly overall correlated with Passengers_In and 7 other fieldsHigh correlation
Mail_Total_(tonnes) is highly overall correlated with Passengers_In and 7 other fieldsHigh correlation
Passengers_In has 9946 (11.8%) zerosZeros
Freight_In_(tonnes) has 21606 (25.6%) zerosZeros
Mail_In_(tonnes) has 47513 (56.3%) zerosZeros
Passengers_Out has 9077 (10.7%) zerosZeros
Freight_Out_(tonnes) has 21045 (24.9%) zerosZeros
Mail_Out_(tonnes) has 44812 (53.1%) zerosZeros
Passengers_Total has 5904 (7.0%) zerosZeros
Freight_Total_(tonnes) has 16001 (18.9%) zerosZeros
Mail_Total_(tonnes) has 36538 (43.3%) zerosZeros

Reproduction

Analysis started2023-03-04 09:51:11.973210
Analysis finished2023-03-04 09:51:34.920152
Duration22.95 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Month
Categorical

Distinct453
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size660.0 KiB
Jun-00
 
314
Oct-99
 
311
Nov-99
 
311
Dec-99
 
311
Apr-00
 
307
Other values (448)
82910 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters506784
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJan-85
2nd rowJan-85
3rd rowJan-85
4th rowJan-85
5th rowJan-85

Common Values

ValueCountFrequency (%)
Jun-00 314
 
0.4%
Oct-99 311
 
0.4%
Nov-99 311
 
0.4%
Dec-99 311
 
0.4%
Apr-00 307
 
0.4%
May-00 306
 
0.4%
Feb-00 305
 
0.4%
Mar-00 304
 
0.4%
Oct-00 291
 
0.3%
Jan-00 290
 
0.3%
Other values (443) 81414
96.4%

Length

2023-03-04T17:51:34.988959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jun-00 314
 
0.4%
dec-99 311
 
0.4%
oct-99 311
 
0.4%
nov-99 311
 
0.4%
apr-00 307
 
0.4%
may-00 306
 
0.4%
feb-00 305
 
0.4%
mar-00 304
 
0.4%
oct-00 291
 
0.3%
jan-00 290
 
0.3%
Other values (443) 81414
96.4%

Most occurring characters

ValueCountFrequency (%)
- 84464
16.7%
9 37778
 
7.5%
0 31157
 
6.1%
1 27847
 
5.5%
e 21233
 
4.2%
a 21188
 
4.2%
J 21156
 
4.2%
u 21063
 
4.2%
8 20737
 
4.1%
n 14125
 
2.8%
Other values (23) 206036
40.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 168928
33.3%
Lowercase Letter 168928
33.3%
Dash Punctuation 84464
16.7%
Uppercase Letter 84464
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 21233
12.6%
a 21188
12.5%
u 21063
12.5%
n 14125
8.4%
c 14124
8.4%
r 14109
8.4%
p 14032
8.3%
b 7087
 
4.2%
t 7034
 
4.2%
l 7031
 
4.2%
Other values (4) 27902
16.5%
Decimal Number
ValueCountFrequency (%)
9 37778
22.4%
0 31157
18.4%
1 27847
16.5%
8 20737
12.3%
2 11936
 
7.1%
7 9371
 
5.5%
6 9038
 
5.4%
5 8477
 
5.0%
3 6324
 
3.7%
4 6263
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
J 21156
25.0%
M 14080
16.7%
A 13991
16.6%
D 7090
 
8.4%
F 7087
 
8.4%
S 7056
 
8.4%
O 7034
 
8.3%
N 6970
 
8.3%
Dash Punctuation
ValueCountFrequency (%)
- 84464
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 253392
50.0%
Latin 253392
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 21233
 
8.4%
a 21188
 
8.4%
J 21156
 
8.3%
u 21063
 
8.3%
n 14125
 
5.6%
c 14124
 
5.6%
r 14109
 
5.6%
M 14080
 
5.6%
p 14032
 
5.5%
A 13991
 
5.5%
Other values (12) 84291
33.3%
Common
ValueCountFrequency (%)
- 84464
33.3%
9 37778
14.9%
0 31157
 
12.3%
1 27847
 
11.0%
8 20737
 
8.2%
2 11936
 
4.7%
7 9371
 
3.7%
6 9038
 
3.6%
5 8477
 
3.3%
3 6324
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 506784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 84464
16.7%
9 37778
 
7.5%
0 31157
 
6.1%
1 27847
 
5.5%
e 21233
 
4.2%
a 21188
 
4.2%
J 21156
 
4.2%
u 21063
 
4.2%
8 20737
 
4.1%
n 14125
 
2.8%
Other values (23) 206036
40.7%

AustralianPort
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size660.0 KiB
Sydney
23688 
Melbourne
16963 
Brisbane
13916 
Perth
9019 
Cairns
6811 
Other values (14)
14067 

Length

Max length22
Median length18
Mean length7.2322883
Min length5

Characters and Unicode

Total characters610868
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdelaide
2nd rowAdelaide
3rd rowAdelaide
4th rowAdelaide
5th rowAdelaide

Common Values

ValueCountFrequency (%)
Sydney 23688
28.0%
Melbourne 16963
20.1%
Brisbane 13916
16.5%
Perth 9019
 
10.7%
Cairns 6811
 
8.1%
Adelaide 6047
 
7.2%
Darwin 4446
 
5.3%
Gold Coast 1016
 
1.2%
Townsville 660
 
0.8%
Gold Coast/Coolangatta 503
 
0.6%
Other values (9) 1395
 
1.7%

Length

2023-03-04T17:51:35.097663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sydney 23688
27.3%
melbourne 16963
19.5%
brisbane 13916
16.0%
perth 9019
 
10.4%
cairns 6811
 
7.8%
adelaide 6047
 
7.0%
darwin 4446
 
5.1%
gold 1519
 
1.7%
coast 1057
 
1.2%
townsville 660
 
0.8%
Other values (13) 2776
 
3.2%

Most occurring characters

ValueCountFrequency (%)
e 93887
15.4%
n 67952
11.1%
r 52496
 
8.6%
y 47376
 
7.8%
d 38377
 
6.3%
a 35869
 
5.9%
i 32001
 
5.2%
b 31389
 
5.1%
l 27759
 
4.5%
S 23729
 
3.9%
Other values (27) 160033
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 520522
85.2%
Uppercase Letter 87405
 
14.3%
Space Separator 2438
 
0.4%
Other Punctuation 503
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 93887
18.0%
n 67952
13.1%
r 52496
10.1%
y 47376
9.1%
d 38377
7.4%
a 35869
 
6.9%
i 32001
 
6.1%
b 31389
 
6.0%
l 27759
 
5.3%
s 23668
 
4.5%
Other values (12) 69748
13.4%
Uppercase Letter
ValueCountFrequency (%)
S 23729
27.1%
M 16963
19.4%
B 13953
16.0%
P 9306
 
10.6%
C 9048
 
10.4%
A 6047
 
6.9%
D 4446
 
5.1%
G 1519
 
1.7%
T 749
 
0.9%
H 614
 
0.7%
Other values (3) 1031
 
1.2%
Space Separator
ValueCountFrequency (%)
2438
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 503
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 607927
99.5%
Common 2941
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 93887
15.4%
n 67952
11.2%
r 52496
 
8.6%
y 47376
 
7.8%
d 38377
 
6.3%
a 35869
 
5.9%
i 32001
 
5.3%
b 31389
 
5.2%
l 27759
 
4.6%
S 23729
 
3.9%
Other values (25) 157092
25.8%
Common
ValueCountFrequency (%)
2438
82.9%
/ 503
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 610868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 93887
15.4%
n 67952
11.1%
r 52496
 
8.6%
y 47376
 
7.8%
d 38377
 
6.3%
a 35869
 
5.9%
i 32001
 
5.2%
b 31389
 
5.1%
l 27759
 
4.5%
S 23729
 
3.9%
Other values (27) 160033
26.2%

ForeignPort
Categorical

Distinct192
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size660.0 KiB
Auckland
 
3566
Singapore
 
3394
Denpasar
 
3033
Hong Kong
 
2913
Kuala Lumpur
 
2583
Other values (187)
68975 

Length

Max length22
Median length15
Mean length8.0699825
Min length3

Characters and Unicode

Total characters681623
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowAuckland
2nd rowBahrain
3rd rowBombay
4th rowFrankfurt
5th rowLondon

Common Values

ValueCountFrequency (%)
Auckland 3566
 
4.2%
Singapore 3394
 
4.0%
Denpasar 3033
 
3.6%
Hong Kong 2913
 
3.4%
Kuala Lumpur 2583
 
3.1%
Tokyo 2475
 
2.9%
Bangkok 2413
 
2.9%
Christchurch 2286
 
2.7%
Los Angeles 2104
 
2.5%
London 2074
 
2.5%
Other values (182) 57623
68.2%

Length

2023-03-04T17:51:35.219359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
auckland 3566
 
3.4%
singapore 3394
 
3.3%
denpasar 3033
 
2.9%
port 2995
 
2.9%
hong 2913
 
2.8%
kong 2913
 
2.8%
kuala 2583
 
2.5%
lumpur 2583
 
2.5%
tokyo 2475
 
2.4%
bangkok 2413
 
2.3%
Other values (215) 75072
72.2%

Most occurring characters

ValueCountFrequency (%)
a 78189
 
11.5%
n 60106
 
8.8%
o 59603
 
8.7%
r 40962
 
6.0%
e 40364
 
5.9%
u 38882
 
5.7%
i 35341
 
5.2%
g 25913
 
3.8%
l 23578
 
3.5%
s 21842
 
3.2%
Other values (46) 256843
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 558206
81.9%
Uppercase Letter 103856
 
15.2%
Space Separator 19476
 
2.9%
Other Punctuation 81
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 78189
14.0%
n 60106
10.8%
o 59603
10.7%
r 40962
 
7.3%
e 40364
 
7.2%
u 38882
 
7.0%
i 35341
 
6.3%
g 25913
 
4.6%
l 23578
 
4.2%
s 21842
 
3.9%
Other values (16) 133426
23.9%
Uppercase Letter
ValueCountFrequency (%)
A 9737
 
9.4%
S 9154
 
8.8%
H 8506
 
8.2%
B 8048
 
7.7%
L 7329
 
7.1%
D 7000
 
6.7%
M 6358
 
6.1%
K 6310
 
6.1%
N 6264
 
6.0%
P 5813
 
5.6%
Other values (16) 29337
28.2%
Space Separator
ValueCountFrequency (%)
19476
100.0%
Other Punctuation
ValueCountFrequency (%)
' 81
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 662062
97.1%
Common 19561
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 78189
 
11.8%
n 60106
 
9.1%
o 59603
 
9.0%
r 40962
 
6.2%
e 40364
 
6.1%
u 38882
 
5.9%
i 35341
 
5.3%
g 25913
 
3.9%
l 23578
 
3.6%
s 21842
 
3.3%
Other values (42) 237282
35.8%
Common
ValueCountFrequency (%)
19476
99.6%
' 81
 
0.4%
( 2
 
< 0.1%
) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 681623
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 78189
 
11.5%
n 60106
 
8.8%
o 59603
 
8.7%
r 40962
 
6.0%
e 40364
 
5.9%
u 38882
 
5.7%
i 35341
 
5.2%
g 25913
 
3.8%
l 23578
 
3.5%
s 21842
 
3.2%
Other values (46) 256843
37.7%

Country
Categorical

Distinct74
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size660.0 KiB
New Zealand
10066 
USA
8879 
Japan
5612 
Indonesia
5366 
China
 
4435
Other values (69)
50106 

Length

Max length20
Median length14
Mean length7.8382033
Min length2

Characters and Unicode

Total characters662046
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowNew Zealand
2nd rowBahrain
3rd rowIndia
4th rowGermany
5th rowUK

Common Values

ValueCountFrequency (%)
New Zealand 10066
 
11.9%
USA 8879
 
10.5%
Japan 5612
 
6.6%
Indonesia 5366
 
6.4%
China 4435
 
5.3%
Singapore 3394
 
4.0%
Thailand 3088
 
3.7%
Malaysia 2937
 
3.5%
UK 2871
 
3.4%
Canada 2325
 
2.8%
Other values (64) 35491
42.0%

Length

2023-03-04T17:51:35.342031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new 13423
 
12.0%
zealand 10066
 
9.0%
usa 8879
 
7.9%
japan 5612
 
5.0%
indonesia 5366
 
4.8%
china 4435
 
4.0%
singapore 3394
 
3.0%
thailand 3088
 
2.8%
malaysia 2937
 
2.6%
hong 2913
 
2.6%
Other values (75) 51787
46.3%

Most occurring characters

ValueCountFrequency (%)
a 105814
16.0%
n 67765
 
10.2%
e 54615
 
8.2%
i 49743
 
7.5%
d 27568
 
4.2%
27436
 
4.1%
l 24117
 
3.6%
o 22653
 
3.4%
r 20003
 
3.0%
s 17587
 
2.7%
Other values (43) 244745
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 497373
75.1%
Uppercase Letter 134883
 
20.4%
Space Separator 27436
 
4.1%
Open Punctuation 1177
 
0.2%
Close Punctuation 1177
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 105814
21.3%
n 67765
13.6%
e 54615
11.0%
i 49743
10.0%
d 27568
 
5.5%
l 24117
 
4.8%
o 22653
 
4.6%
r 20003
 
4.0%
s 17587
 
3.5%
w 15414
 
3.1%
Other values (15) 92094
18.5%
Uppercase Letter
ValueCountFrequency (%)
S 16526
12.3%
N 14759
10.9%
A 14541
10.8%
U 14069
10.4%
Z 10760
 
8.0%
I 9150
 
6.8%
C 8752
 
6.5%
K 6911
 
5.1%
J 5612
 
4.2%
T 5532
 
4.1%
Other values (15) 28271
21.0%
Space Separator
ValueCountFrequency (%)
27436
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1177
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1177
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 632256
95.5%
Common 29790
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 105814
16.7%
n 67765
 
10.7%
e 54615
 
8.6%
i 49743
 
7.9%
d 27568
 
4.4%
l 24117
 
3.8%
o 22653
 
3.6%
r 20003
 
3.2%
s 17587
 
2.8%
S 16526
 
2.6%
Other values (40) 225865
35.7%
Common
ValueCountFrequency (%)
27436
92.1%
( 1177
 
4.0%
) 1177
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 662046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 105814
16.0%
n 67765
 
10.2%
e 54615
 
8.2%
i 49743
 
7.5%
d 27568
 
4.2%
27436
 
4.1%
l 24117
 
3.6%
o 22653
 
3.4%
r 20003
 
3.0%
s 17587
 
2.7%
Other values (43) 244745
37.0%

Passengers_In
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17287
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4271.1428
Minimum0
Maximum84314
Zeros9946
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size660.0 KiB
2023-03-04T17:51:35.471253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1109
median1157
Q34442
95-th percentile19952
Maximum84314
Range84314
Interquartile range (IQR)4333

Descriptive statistics

Standard deviation8164.7221
Coefficient of variation (CV)1.9116013
Kurtosis17.185949
Mean4271.1428
Median Absolute Deviation (MAD)1156
Skewness3.6861863
Sum3.6075781 × 108
Variance66662687
MonotonicityNot monotonic
2023-03-04T17:51:35.592934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9946
 
11.8%
1 673
 
0.8%
2 594
 
0.7%
4 390
 
0.5%
3 380
 
0.4%
5 306
 
0.4%
6 305
 
0.4%
7 245
 
0.3%
9 240
 
0.3%
8 238
 
0.3%
Other values (17277) 71147
84.2%
ValueCountFrequency (%)
0 9946
11.8%
1 673
 
0.8%
2 594
 
0.7%
3 380
 
0.4%
4 390
 
0.5%
5 306
 
0.4%
6 305
 
0.4%
7 245
 
0.3%
8 238
 
0.3%
9 240
 
0.3%
ValueCountFrequency (%)
84314 1
< 0.1%
83898 1
< 0.1%
83286 1
< 0.1%
82151 1
< 0.1%
80960 1
< 0.1%
80444 1
< 0.1%
79916 1
< 0.1%
79691 1
< 0.1%
79614 1
< 0.1%
79046 1
< 0.1%

Freight_In_(tonnes)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47861
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.61183
Minimum0
Maximum6764.923
Zeros21606
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size660.0 KiB
2023-03-04T17:51:35.719590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13.2955
Q3128.14525
95-th percentile848.32255
Maximum6764.923
Range6764.923
Interquartile range (IQR)128.14525

Descriptive statistics

Standard deviation392.51278
Coefficient of variation (CV)2.4438597
Kurtosis32.720667
Mean160.61183
Median Absolute Deviation (MAD)13.2955
Skewness4.7773496
Sum13565918
Variance154066.28
MonotonicityNot monotonic
2023-03-04T17:51:35.849151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21606
 
25.6%
0.1 105
 
0.1%
0.002 72
 
0.1%
0.01 70
 
0.1%
0.001 69
 
0.1%
0.2 67
 
0.1%
0.003 57
 
0.1%
0.004 54
 
0.1%
0.005 53
 
0.1%
0.007 48
 
0.1%
Other values (47851) 62263
73.7%
ValueCountFrequency (%)
0 21606
25.6%
0.001 69
 
0.1%
0.002 72
 
0.1%
0.003 57
 
0.1%
0.004 54
 
0.1%
0.005 53
 
0.1%
0.006 33
 
< 0.1%
0.007 48
 
0.1%
0.008 43
 
0.1%
0.009 35
 
< 0.1%
ValueCountFrequency (%)
6764.923 1
< 0.1%
6725.072 1
< 0.1%
6615.954 1
< 0.1%
6493.122 1
< 0.1%
6469.027 1
< 0.1%
6368.841 1
< 0.1%
6260.926 1
< 0.1%
6253.855 1
< 0.1%
6104.398 1
< 0.1%
5909.797 1
< 0.1%

Mail_In_(tonnes)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17652
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5504775
Minimum0
Maximum393.705
Zeros47513
Zeros (%)56.3%
Negative0
Negative (%)0.0%
Memory size660.0 KiB
2023-03-04T17:51:35.990512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.64625
95-th percentile48.4543
Maximum393.705
Range393.705
Interquartile range (IQR)1.64625

Descriptive statistics

Standard deviation24.237905
Coefficient of variation (CV)3.2101155
Kurtosis37.468186
Mean7.5504775
Median Absolute Deviation (MAD)0
Skewness5.3839626
Sum637743.53
Variance587.47603
MonotonicityNot monotonic
2023-03-04T17:51:36.240511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47513
56.3%
0.001 288
 
0.3%
0.002 166
 
0.2%
0.004 147
 
0.2%
0.006 126
 
0.1%
0.009 114
 
0.1%
0.005 108
 
0.1%
0.003 107
 
0.1%
0.008 100
 
0.1%
0.007 94
 
0.1%
Other values (17642) 35701
42.3%
ValueCountFrequency (%)
0 47513
56.3%
0.001 288
 
0.3%
0.002 166
 
0.2%
0.003 107
 
0.1%
0.004 147
 
0.2%
0.005 108
 
0.1%
0.006 126
 
0.1%
0.007 94
 
0.1%
0.008 100
 
0.1%
0.009 114
 
0.1%
ValueCountFrequency (%)
393.705 1
< 0.1%
350.756 1
< 0.1%
349.801 1
< 0.1%
348.758 1
< 0.1%
340.111 1
< 0.1%
337.882 1
< 0.1%
323.973 1
< 0.1%
317.128 1
< 0.1%
314.621 1
< 0.1%
313.785 1
< 0.1%

Passengers_Out
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17243
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4211.6944
Minimum0
Maximum83168
Zeros9077
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size660.0 KiB
2023-03-04T17:51:36.382316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1115
median1139
Q34294.25
95-th percentile19825
Maximum83168
Range83168
Interquartile range (IQR)4179.25

Descriptive statistics

Standard deviation8032.9435
Coefficient of variation (CV)1.907295
Kurtosis16.956186
Mean4211.6944
Median Absolute Deviation (MAD)1136
Skewness3.6545525
Sum3.5573655 × 108
Variance64528181
MonotonicityNot monotonic
2023-03-04T17:51:36.505363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9077
 
10.7%
1 705
 
0.8%
2 594
 
0.7%
3 425
 
0.5%
4 412
 
0.5%
5 334
 
0.4%
6 294
 
0.3%
8 259
 
0.3%
7 258
 
0.3%
10 231
 
0.3%
Other values (17233) 71875
85.1%
ValueCountFrequency (%)
0 9077
10.7%
1 705
 
0.8%
2 594
 
0.7%
3 425
 
0.5%
4 412
 
0.5%
5 334
 
0.4%
6 294
 
0.3%
7 258
 
0.3%
8 259
 
0.3%
9 206
 
0.2%
ValueCountFrequency (%)
83168 1
< 0.1%
83117 1
< 0.1%
82766 1
< 0.1%
82560 1
< 0.1%
82242 1
< 0.1%
82129 1
< 0.1%
80894 1
< 0.1%
78836 1
< 0.1%
78738 1
< 0.1%
78138 1
< 0.1%

Freight_Out_(tonnes)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47726
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.27271
Minimum0
Maximum4996.582
Zeros21045
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size660.0 KiB
2023-03-04T17:51:36.633003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.002
median13.322
Q3112.035
95-th percentile752.7987
Maximum4996.582
Range4996.582
Interquartile range (IQR)112.033

Descriptive statistics

Standard deviation358.84141
Coefficient of variation (CV)2.4872439
Kurtosis26.287902
Mean144.27271
Median Absolute Deviation (MAD)13.322
Skewness4.59457
Sum12185850
Variance128767.16
MonotonicityNot monotonic
2023-03-04T17:51:36.763673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21045
 
24.9%
0.1 77
 
0.1%
0.001 67
 
0.1%
0.01 60
 
0.1%
0.02 55
 
0.1%
0.002 49
 
0.1%
0.005 45
 
0.1%
0.015 45
 
0.1%
0.004 43
 
0.1%
0.04 43
 
0.1%
Other values (47716) 62935
74.5%
ValueCountFrequency (%)
0 21045
24.9%
0.001 67
 
0.1%
0.002 49
 
0.1%
0.003 38
 
< 0.1%
0.004 43
 
0.1%
0.005 45
 
0.1%
0.006 43
 
0.1%
0.007 34
 
< 0.1%
0.008 39
 
< 0.1%
0.009 40
 
< 0.1%
ValueCountFrequency (%)
4996.582 1
< 0.1%
4422.703 1
< 0.1%
4380.961 1
< 0.1%
4319.344 1
< 0.1%
4287.038 1
< 0.1%
4211.761 1
< 0.1%
4179.273 1
< 0.1%
4141.178 1
< 0.1%
4089.763 1
< 0.1%
4035.849 1
< 0.1%

Mail_Out_(tonnes)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15497
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4907096
Minimum0
Maximum382.229
Zeros44812
Zeros (%)53.1%
Negative0
Negative (%)0.0%
Memory size660.0 KiB
2023-03-04T17:51:36.900291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.508
95-th percentile27.5669
Maximum382.229
Range382.229
Interquartile range (IQR)1.508

Descriptive statistics

Standard deviation14.230078
Coefficient of variation (CV)3.1687815
Kurtosis65.516284
Mean4.4907096
Median Absolute Deviation (MAD)0
Skewness6.3062514
Sum379303.3
Variance202.49511
MonotonicityNot monotonic
2023-03-04T17:51:37.031553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44812
53.1%
0.001 219
 
0.3%
0.004 146
 
0.2%
0.003 136
 
0.2%
0.002 135
 
0.2%
0.005 129
 
0.2%
0.008 119
 
0.1%
0.011 117
 
0.1%
0.006 110
 
0.1%
0.012 107
 
0.1%
Other values (15487) 38434
45.5%
ValueCountFrequency (%)
0 44812
53.1%
0.001 219
 
0.3%
0.002 135
 
0.2%
0.003 136
 
0.2%
0.004 146
 
0.2%
0.005 129
 
0.2%
0.006 110
 
0.1%
0.007 107
 
0.1%
0.008 119
 
0.1%
0.009 103
 
0.1%
ValueCountFrequency (%)
382.229 1
< 0.1%
359.956 1
< 0.1%
358.67 1
< 0.1%
353.028 1
< 0.1%
323.718 1
< 0.1%
298.339 1
< 0.1%
288.633 1
< 0.1%
254.821 1
< 0.1%
249.821 1
< 0.1%
246.197 1
< 0.1%

Passengers_Total
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24195
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8482.8372
Minimum0
Maximum162036
Zeros5904
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size660.0 KiB
2023-03-04T17:51:37.171862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1246
median2310.5
Q38794
95-th percentile39649.65
Maximum162036
Range162036
Interquartile range (IQR)8548

Descriptive statistics

Standard deviation16125.377
Coefficient of variation (CV)1.9009415
Kurtosis16.808226
Mean8482.8372
Median Absolute Deviation (MAD)2295.5
Skewness3.6502182
Sum7.1649436 × 108
Variance2.600278 × 108
MonotonicityNot monotonic
2023-03-04T17:51:37.297525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5904
 
7.0%
1 713
 
0.8%
2 651
 
0.8%
3 430
 
0.5%
4 382
 
0.5%
5 348
 
0.4%
6 317
 
0.4%
7 270
 
0.3%
8 254
 
0.3%
10 235
 
0.3%
Other values (24185) 74960
88.7%
ValueCountFrequency (%)
0 5904
7.0%
1 713
 
0.8%
2 651
 
0.8%
3 430
 
0.5%
4 382
 
0.5%
5 348
 
0.4%
6 317
 
0.4%
7 270
 
0.3%
8 254
 
0.3%
9 228
 
0.3%
ValueCountFrequency (%)
162036 1
< 0.1%
160623 1
< 0.1%
160620 1
< 0.1%
158788 1
< 0.1%
155739 1
< 0.1%
155277 1
< 0.1%
155006 1
< 0.1%
154936 1
< 0.1%
153274 1
< 0.1%
152700 1
< 0.1%

Freight_Total_(tonnes)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55901
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304.88454
Minimum0
Maximum9889.553
Zeros16001
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size660.0 KiB
2023-03-04T17:51:37.436880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.61975
median39.643
Q3259.63425
95-th percentile1624.476
Maximum9889.553
Range9889.553
Interquartile range (IQR)259.0145

Descriptive statistics

Standard deviation709.13079
Coefficient of variation (CV)2.3258995
Kurtosis23.951986
Mean304.88454
Median Absolute Deviation (MAD)39.643
Skewness4.3263864
Sum25751768
Variance502866.47
MonotonicityNot monotonic
2023-03-04T17:51:37.567512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16001
 
18.9%
0.1 65
 
0.1%
0.01 61
 
0.1%
0.001 56
 
0.1%
0.002 54
 
0.1%
0.004 42
 
< 0.1%
0.009 41
 
< 0.1%
0.02 40
 
< 0.1%
0.17 39
 
< 0.1%
0.012 39
 
< 0.1%
Other values (55891) 68026
80.5%
ValueCountFrequency (%)
0 16001
18.9%
0.001 56
 
0.1%
0.002 54
 
0.1%
0.003 38
 
< 0.1%
0.004 42
 
< 0.1%
0.005 37
 
< 0.1%
0.006 38
 
< 0.1%
0.007 32
 
< 0.1%
0.008 35
 
< 0.1%
0.009 41
 
< 0.1%
ValueCountFrequency (%)
9889.553 1
< 0.1%
9646.309 1
< 0.1%
9561.433 1
< 0.1%
9315.425 1
< 0.1%
9026.831 1
< 0.1%
8959.197 1
< 0.1%
8955.371 1
< 0.1%
8589.115 1
< 0.1%
8580.798 1
< 0.1%
8396.16 1
< 0.1%

Mail_Total_(tonnes)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22038
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.041187
Minimum0
Maximum566.993
Zeros36538
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size660.0 KiB
2023-03-04T17:51:37.700239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.105
Q34.81325
95-th percentile74.27915
Maximum566.993
Range566.993
Interquartile range (IQR)4.81325

Descriptive statistics

Standard deviation34.578751
Coefficient of variation (CV)2.8717061
Kurtosis30.138195
Mean12.041187
Median Absolute Deviation (MAD)0.105
Skewness4.8147981
Sum1017046.8
Variance1195.69
MonotonicityNot monotonic
2023-03-04T17:51:37.821003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36538
43.3%
0.001 244
 
0.3%
0.004 132
 
0.2%
0.002 132
 
0.2%
0.005 120
 
0.1%
0.006 115
 
0.1%
0.011 112
 
0.1%
0.012 110
 
0.1%
0.013 106
 
0.1%
0.003 104
 
0.1%
Other values (22028) 46751
55.4%
ValueCountFrequency (%)
0 36538
43.3%
0.001 244
 
0.3%
0.002 132
 
0.2%
0.003 104
 
0.1%
0.004 132
 
0.2%
0.005 120
 
0.1%
0.006 115
 
0.1%
0.007 98
 
0.1%
0.008 98
 
0.1%
0.009 87
 
0.1%
ValueCountFrequency (%)
566.993 1
< 0.1%
547.83 1
< 0.1%
541.348 1
< 0.1%
489.908 1
< 0.1%
483.767 1
< 0.1%
474.902 1
< 0.1%
456.677 1
< 0.1%
444.414 1
< 0.1%
441.447 1
< 0.1%
429.292 1
< 0.1%

Year
Real number (ℝ)

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.9032
Minimum1985
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size660.0 KiB
2023-03-04T17:51:37.948975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1985
5-th percentile1987
Q11994
median2000
Q32011
95-th percentile2019
Maximum2022
Range37
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.38151
Coefficient of variation (CV)0.0051858201
Kurtosis-1.0563394
Mean2001.9032
Median Absolute Deviation (MAD)8
Skewness0.23810968
Sum1.6908876 × 108
Variance107.77575
MonotonicityIncreasing
2023-03-04T17:51:38.066978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2000 3502
 
4.1%
1999 3407
 
4.0%
1998 3162
 
3.7%
1997 3157
 
3.7%
1996 3035
 
3.6%
2001 2970
 
3.5%
1995 2807
 
3.3%
2002 2769
 
3.3%
1993 2656
 
3.1%
1994 2600
 
3.1%
Other values (28) 54399
64.4%
ValueCountFrequency (%)
1985 1944
2.3%
1986 2224
2.6%
1987 2284
2.7%
1988 2361
2.8%
1989 2369
2.8%
1990 2471
2.9%
1991 2281
2.7%
1992 2469
2.9%
1993 2656
3.1%
1994 2600
3.1%
ValueCountFrequency (%)
2022 1123
1.3%
2021 1271
1.5%
2020 1373
1.6%
2019 2219
2.6%
2018 2240
2.7%
2017 2106
2.5%
2016 1949
2.3%
2015 1828
2.2%
2014 1786
2.1%
2013 1848
2.2%

Month_num
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4921505
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size660.0 KiB
2023-03-04T17:51:38.183456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4585515
Coefficient of variation (CV)0.53272817
Kurtosis-1.2207545
Mean6.4921505
Median Absolute Deviation (MAD)3
Skewness0.002109707
Sum548353
Variance11.961578
MonotonicityNot monotonic
2023-03-04T17:51:38.273243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 7133
8.4%
1 7108
8.4%
12 7090
8.4%
2 7087
8.4%
9 7056
8.4%
10 7034
8.3%
7 7031
8.3%
6 7017
8.3%
8 7015
8.3%
4 6976
8.3%
Other values (2) 13917
16.5%
ValueCountFrequency (%)
1 7108
8.4%
2 7087
8.4%
3 7133
8.4%
4 6976
8.3%
5 6947
8.2%
6 7017
8.3%
7 7031
8.3%
8 7015
8.3%
9 7056
8.4%
10 7034
8.3%
ValueCountFrequency (%)
12 7090
8.4%
11 6970
8.3%
10 7034
8.3%
9 7056
8.4%
8 7015
8.3%
7 7031
8.3%
6 7017
8.3%
5 6947
8.2%
4 6976
8.3%
3 7133
8.4%

passenger_in_out
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size660.0 KiB
IN
39810 
OUT
38423 
SAME
6231 

Length

Max length4
Median length3
Mean length2.602446
Min length2

Characters and Unicode

Total characters219813
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIN
2nd rowIN
3rd rowIN
4th rowOUT
5th rowIN

Common Values

ValueCountFrequency (%)
IN 39810
47.1%
OUT 38423
45.5%
SAME 6231
 
7.4%

Length

2023-03-04T17:51:38.494617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-04T17:51:38.621250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
in 39810
47.1%
out 38423
45.5%
same 6231
 
7.4%

Most occurring characters

ValueCountFrequency (%)
I 39810
18.1%
N 39810
18.1%
O 38423
17.5%
U 38423
17.5%
T 38423
17.5%
S 6231
 
2.8%
A 6231
 
2.8%
M 6231
 
2.8%
E 6231
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 219813
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 39810
18.1%
N 39810
18.1%
O 38423
17.5%
U 38423
17.5%
T 38423
17.5%
S 6231
 
2.8%
A 6231
 
2.8%
M 6231
 
2.8%
E 6231
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 219813
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 39810
18.1%
N 39810
18.1%
O 38423
17.5%
U 38423
17.5%
T 38423
17.5%
S 6231
 
2.8%
A 6231
 
2.8%
M 6231
 
2.8%
E 6231
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 219813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 39810
18.1%
N 39810
18.1%
O 38423
17.5%
U 38423
17.5%
T 38423
17.5%
S 6231
 
2.8%
A 6231
 
2.8%
M 6231
 
2.8%
E 6231
 
2.8%

Interactions

2023-03-04T17:51:32.548426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:15.846364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:17.621460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:19.338601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:21.175357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:22.739654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:24.248648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:26.116610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:27.772095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:29.423861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:30.974671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:32.810727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:16.025884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:17.756100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:19.507149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:21.322963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:22.869281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:24.387303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:26.260247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:27.904767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:29.561499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:31.112334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:32.964671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:16.185458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:17.942600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:19.659769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:21.460623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:23.009933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:24.552853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:26.417804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:28.049356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:29.707082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:31.257932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:33.111309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:16.346049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:18.091203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:19.835504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:21.621163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:23.150527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:24.905890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:26.572391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:28.192996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:29.851715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:31.412499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:33.236942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:16.488736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:18.235817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:19.981116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:21.756802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:23.280180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:25.075434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:26.712788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:28.324644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:29.985500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:31.548138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:33.365300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:16.728663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:18.399386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:20.138693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:21.898421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:23.413849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:25.220226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:26.860393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:28.465755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:30.123320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:31.689786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:33.498980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:16.874272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:18.577901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:20.285303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:22.051546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:23.561455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:25.370472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:27.016973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:28.617374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:30.270454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:31.833404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:33.644113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:17.064334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:18.738618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:20.438325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:22.195178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:23.710294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:25.525618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:27.175522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:28.882806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:30.417086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:31.984970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:33.772757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:17.210561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:18.902179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:20.583974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:22.329463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:23.845965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:25.672225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:27.323133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:29.017954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:30.556740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:32.121715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:33.897946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:17.356197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:19.045840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:20.867359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:22.463133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:23.978578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:25.820846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:27.467914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:29.151597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:30.693936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:32.258208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:34.029851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:17.491834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:19.190997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:21.016808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:22.606745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:24.120018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:25.973992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:27.623518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:29.292220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:30.840030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-04T17:51:32.415810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-03-04T17:51:38.720985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Passengers_InFreight_In_(tonnes)Mail_In_(tonnes)Passengers_OutFreight_Out_(tonnes)Mail_Out_(tonnes)Passengers_TotalFreight_Total_(tonnes)Mail_Total_(tonnes)YearMonth_numAustralianPortCountrypassenger_in_out
Passengers_In1.0000.7270.5810.9700.7400.4770.9910.7190.5680.330-0.0030.0830.1520.085
Freight_In_(tonnes)0.7271.0000.6630.7210.8070.5020.7240.9520.6420.2800.0130.0770.1350.016
Mail_In_(tonnes)0.5810.6631.0000.5820.6390.6500.5840.6610.8830.1110.0050.0830.0930.050
Passengers_Out0.9700.7210.5821.0000.7490.4780.9910.7210.5700.3370.0050.0850.1540.086
Freight_Out_(tonnes)0.7400.8070.6390.7491.0000.5450.7480.9140.6400.2230.0090.0630.1720.049
Mail_Out_(tonnes)0.4770.5020.6500.4780.5451.0000.4790.5170.860-0.0490.0080.0560.0840.039
Passengers_Total0.9910.7240.5840.9910.7480.4791.0000.7210.5720.3330.0010.0850.1560.083
Freight_Total_(tonnes)0.7190.9520.6610.7210.9140.5170.7211.0000.6450.2760.0120.0730.1650.038
Mail_Total_(tonnes)0.5680.6420.8830.5700.6400.8600.5720.6451.0000.0550.0100.0830.1010.055
Year0.3300.2800.1110.3370.223-0.0490.3330.2760.0551.000-0.0180.1280.2000.131
Month_num-0.0030.0130.0050.0050.0090.0080.0010.0120.010-0.0181.0000.0000.0000.209
AustralianPort0.0830.0770.0830.0850.0630.0560.0850.0730.0830.1280.0001.0000.1790.124
Country0.1520.1350.0930.1540.1720.0840.1560.1650.1010.2000.0000.1791.0000.276
passenger_in_out0.0850.0160.0500.0860.0490.0390.0830.0380.0550.1310.2090.1240.2761.000

Missing values

2023-03-04T17:51:34.234824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-04T17:51:34.588931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MonthAustralianPortForeignPortCountryPassengers_InFreight_In_(tonnes)Mail_In_(tonnes)Passengers_OutFreight_Out_(tonnes)Mail_Out_(tonnes)Passengers_TotalFreight_Total_(tonnes)Mail_Total_(tonnes)YearMonth_numpassenger_in_out
0Jan-85AdelaideAucklandNew Zealand151342.1670.31198518.7040.924249860.8711.23519851IN
1Jan-85AdelaideBahrainBahrain120.0000.00050.0330.000170.0330.00019851IN
2Jan-85AdelaideBombayIndia70.0000.00050.0000.000120.0000.00019851IN
3Jan-85AdelaideFrankfurtGermany1150.0090.0001710.0000.2482860.0090.24819851OUT
4Jan-85AdelaideLondonUK15672.8000.000147210.6182.487303913.4182.48719851IN
5Jan-85AdelaideMuscatOman170.0000.000140.1000.000310.1000.00019851IN
6Jan-85AdelaideRomeItaly790.0050.000440.0000.0001230.0050.00019851IN
7Jan-85AdelaideSingaporeSingapore249637.3450.0002037133.2030.1124533170.5480.11219851IN
8Jan-85BrisbaneAbu DhabiUnited Arab Emirates00.0000.00030.0000.00030.0000.00019851OUT
9Jan-85BrisbaneAucklandNew Zealand7157223.2580.671565233.0323.21812809256.2903.88919851IN
MonthAustralianPortForeignPortCountryPassengers_InFreight_In_(tonnes)Mail_In_(tonnes)Passengers_OutFreight_Out_(tonnes)Mail_Out_(tonnes)Passengers_TotalFreight_Total_(tonnes)Mail_Total_(tonnes)YearMonth_numpassenger_in_out
84454Sep-22SydneyShanghaiChina987685.5740.000856997.9295.26018431683.5035.26020229IN
84455Sep-22SydneyShenzhenChina01315.6490.0000416.1950.00001731.8440.00020229SAME
84456Sep-22SydneySingaporeSingapore710366253.85536.168656072335.2608.7011366438589.11544.86920229IN
84457Sep-22SydneySuvaFiji7320.0000.0007740.0000.00015060.0000.00020229OUT
84458Sep-22SydneyTaipeiTaiwan925784.50468.1211050951.4670.08419751735.97168.20520229OUT
84459Sep-22SydneyTokyoJapan9135558.63859.30810573659.21729.100197081217.85588.40820229OUT
84460Sep-22SydneyVancouverCanada11790263.35715.87011410339.74155.79123200603.09871.66120229IN
84461Sep-22SydneyWellingtonNew Zealand83291.1300.00092584.5210.000175875.6510.00020229OUT
84462Sep-22SydneyXiamenChina132871.5830.29996999.0130.0002297170.5960.29920229IN
84463Sep-22Toowoomba WellcampHong KongHong Kong (SAR)00.0000.0000130.4660.0000130.4660.00020229SAME